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  <h1>Source code for cdt.causality.pairwise.RCC</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;Randomized Causation Coefficient Model.</span>

<span class="sd">Author : David Lopez-Paz</span>
<span class="sd">Ref : Lopez-Paz, David and Muandet, Krikamol and Schölkopf, Bernhard and Tolstikhin, Ilya O,</span>
<span class="sd">     &quot;Towards a Learning Theory of Cause-Effect Inference&quot;, ICML 2015.</span>

<span class="sd">.. MIT License</span>
<span class="sd">..</span>
<span class="sd">.. Copyright (c) 2018 Diviyan Kalainathan</span>
<span class="sd">..</span>
<span class="sd">.. Permission is hereby granted, free of charge, to any person obtaining a copy</span>
<span class="sd">.. of this software and associated documentation files (the &quot;Software&quot;), to deal</span>
<span class="sd">.. in the Software without restriction, including without limitation the rights</span>
<span class="sd">.. to use, copy, modify, merge, publish, distribute, sublicense, and/or sell</span>
<span class="sd">.. copies of the Software, and to permit persons to whom the Software is</span>
<span class="sd">.. furnished to do so, subject to the following conditions:</span>
<span class="sd">..</span>
<span class="sd">.. The above copyright notice and this permission notice shall be included in all</span>
<span class="sd">.. copies or substantial portions of the Software.</span>
<span class="sd">..</span>
<span class="sd">.. THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span>
<span class="sd">.. IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span>
<span class="sd">.. FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span>
<span class="sd">.. AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span>
<span class="sd">.. LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span>
<span class="sd">.. OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span>
<span class="sd">.. SOFTWARE.</span>
<span class="sd">&quot;&quot;&quot;</span>

<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">scale</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span> <span class="k">as</span> <span class="n">CLF</span>
<span class="kn">from</span> <span class="nn">...utils.Settings</span> <span class="kn">import</span> <span class="n">SETTINGS</span>
<span class="kn">import</span> <span class="nn">pandas</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">.model</span> <span class="kn">import</span> <span class="n">PairwiseModel</span>


<div class="viewcode-block" id="RCC"><a class="viewcode-back" href="../../../../causality.html#cdt.causality.pairwise.RCC">[docs]</a><span class="k">class</span> <span class="nc">RCC</span><span class="p">(</span><span class="n">PairwiseModel</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Randomized Causation Coefficient model. 2nd approach in the Fast</span>
<span class="sd">    Causation challenge.</span>

<span class="sd">    **Description:** The Randomized causation coefficient (RCC) relies on the</span>
<span class="sd">    projection of the empirical distributions into a RKHS using random cosine</span>
<span class="sd">    embeddings, then classfies the pairs using a random forest based on those</span>
<span class="sd">    features.</span>

<span class="sd">    **Data Type:** Continuous, Categorical, Mixed</span>

<span class="sd">    **Assumptions:** This method needs a substantial amount of labelled causal</span>
<span class="sd">    pairs to train itself. Its final performance depends on the training set</span>
<span class="sd">    used.</span>

<span class="sd">    Args:</span>
<span class="sd">        rand_coeff (int): number of randomized coefficients</span>
<span class="sd">        nb_estimators (int): number of estimators</span>
<span class="sd">        nb_min_leaves (int): number of min samples leaves of the estimator</span>
<span class="sd">        max_depth (): (optional) max depth of the model</span>
<span class="sd">        s (float): scaling</span>
<span class="sd">        njobs (int): number of jobs to be run on parallel (defaults to ``cdt.SETTINGS.NJOBS``)</span>
<span class="sd">        verbose (bool): verbosity (defaults to ``cdt.SETTINGS.verbose``)</span>

<span class="sd">    .. note::</span>
<span class="sd">       Ref : Lopez-Paz, David and Muandet, Krikamol and Schölkopf, Bernhard and Tolstikhin, Ilya O,</span>
<span class="sd">       &quot;Towards a Learning Theory of Cause-Effect Inference&quot;, ICML 2015.</span>

<span class="sd">    Example:</span>
<span class="sd">        &gt;&gt;&gt; from cdt.causality.pairwise import RCC</span>
<span class="sd">        &gt;&gt;&gt; import networkx as nx</span>
<span class="sd">        &gt;&gt;&gt; import matplotlib.pyplot as plt</span>
<span class="sd">        &gt;&gt;&gt; from cdt.data import load_dataset</span>
<span class="sd">        &gt;&gt;&gt; from sklearn.model_selection import train_test_split</span>
<span class="sd">        &gt;&gt;&gt; data, labels = load_dataset(&#39;tuebingen&#39;)</span>
<span class="sd">        &gt;&gt;&gt; X_tr, X_te, y_tr, y_te = train_test_split(data, labels, train_size=.5)</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; obj = RCC()</span>
<span class="sd">        &gt;&gt;&gt; obj.fit(X_tr, y_tr)</span>
<span class="sd">        &gt;&gt;&gt; # This example uses the predict() method</span>
<span class="sd">        &gt;&gt;&gt; output = obj.predict(X_te)</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; # This example uses the orient_graph() method. The dataset used</span>
<span class="sd">        &gt;&gt;&gt; # can be loaded using the cdt.data module</span>
<span class="sd">        &gt;&gt;&gt; data, graph = load_dataset(&#39;sachs&#39;)</span>
<span class="sd">        &gt;&gt;&gt; output = obj.orient_graph(data, nx.DiGraph(graph))</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; # To view the directed graph run the following command</span>
<span class="sd">        &gt;&gt;&gt; nx.draw_networkx(output, font_size=8)</span>
<span class="sd">        &gt;&gt;&gt; plt.show()</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rand_coeff</span><span class="o">=</span><span class="mi">333</span><span class="p">,</span> <span class="n">nb_estimators</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">nb_min_leaves</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
                 <span class="n">max_depth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">njobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Initialize the model w/ its parameters.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">K</span> <span class="o">=</span> <span class="n">rand_coeff</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">E</span> <span class="o">=</span> <span class="n">nb_estimators</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">L</span> <span class="o">=</span> <span class="n">nb_min_leaves</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">njobs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">SETTINGS</span><span class="o">.</span><span class="n">get_default</span><span class="p">((</span><span class="s1">&#39;njobs&#39;</span><span class="p">,</span> <span class="n">njobs</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;verbose&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">max_depth</span> <span class="o">=</span> <span class="n">max_depth</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">W</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">s</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
                            <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">W2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">s</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
                             <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">clf</span> <span class="o">=</span> <span class="kc">None</span>

<div class="viewcode-block" id="RCC.featurize_row"><a class="viewcode-back" href="../../../../causality.html#cdt.causality.pairwise.RCC.featurize_row">[docs]</a>    <span class="k">def</span> <span class="nf">featurize_row</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot; Projects the causal pair to the RKHS using the sampled kernel approximation.</span>

<span class="sd">        Args:</span>
<span class="sd">            x (np.ndarray): Variable 1</span>
<span class="sd">            y (np.ndarray): Variable 2</span>

<span class="sd">        Returns:</span>
<span class="sd">            np.ndarray: projected empirical distributions into a single fixed-size vector.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
        <span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
        <span class="n">dx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">W2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="p">))))</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">dy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">W2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">y</span><span class="p">,</span> <span class="n">b</span><span class="p">))))</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
        <span class="k">if</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">dx</span><span class="p">)</span> <span class="o">&gt;</span> <span class="nb">sum</span><span class="p">(</span><span class="n">dy</span><span class="p">)):</span>
            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">,</span>
                              <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">W</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">b</span><span class="p">))))</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">,</span>
                              <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">W</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="p">))))</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span></div>

<div class="viewcode-block" id="RCC.fit"><a class="viewcode-back" href="../../../../causality.html#cdt.causality.pairwise.RCC.fit">[docs]</a>    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Train the model.</span>

<span class="sd">        Args:</span>
<span class="sd">            x_tr (pd.DataFrame): CEPC format dataframe containing the pairs</span>
<span class="sd">            y_tr (pd.DataFrame or np.ndarray): labels associated to the pairs</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">featurize_row</span><span class="p">(</span><span class="n">row</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
                                                        <span class="n">row</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()]),</span>
                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">featurize_row</span><span class="p">(</span><span class="n">row</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
                                                        <span class="n">row</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()])))</span>
        <span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">y</span><span class="p">,</span> <span class="o">-</span><span class="n">y</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
        <span class="n">verbose</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="k">else</span> <span class="mi">0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">clf</span> <span class="o">=</span> <span class="n">CLF</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">,</span>
                       <span class="n">min_samples_leaf</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">L</span><span class="p">,</span>
                       <span class="n">n_estimators</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">E</span><span class="p">,</span>
                       <span class="n">max_depth</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">max_depth</span><span class="p">,</span>
                       <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">njobs</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span></div>

<div class="viewcode-block" id="RCC.predict_proba"><a class="viewcode-back" href="../../../../causality.html#cdt.causality.pairwise.RCC.predict_proba">[docs]</a>    <span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot; Predict the causal score using a trained RCC model</span>

<span class="sd">        Args:</span>
<span class="sd">            dataset (tuple): Couple of np.ndarray variables to classify</span>

<span class="sd">        Returns:</span>
<span class="sd">            float: Causation score (Value : 1 if a-&gt;b and -1 if b-&gt;a)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clf</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Model has to be trained before making predictions.&quot;</span><span class="p">)</span>

        <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">dataset</span>
        <span class="n">input_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">featurize_row</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">input_</span><span class="p">)</span></div></div>
</pre></div>

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